Creating & Enabling the Useful Service Discovery Experience : The Perfect Recommendation Does Not Exist

Sammanfattning: We are rapidly entering a world with an immense amount of services and devices available to humans and machines. This is a promising future, however there are at least two major challenges for using these services and devices: (1) they have to be found and (2) after being found, they have to be selected amongst. A significant difficulty lies in not only finding most available services, but presenting the most useful ones. In most cases, there may be too many found services and devices to select from.Service discovery needs to become more aimed towards humans and less towards machines. The service discovery challenge is especially prevalent in ubiquitous computing. In particular, service and device flux, human overloading, and service relevance are crucial. This thesis addresses the quality of use of services and devices, by introducing a sophisticated discovery model through the use of new layers in service discovery. This model allows use of services and devices when current automated service discovery and selection would be impractical by providing service suggestions based on user activities, domain knowledge, and world knowledge. To explore what happens when such a system is in place, a wizard of oz study was conducted in a command and control setting.To address service discovery in ubiquitous computing new layers and a test platform were developed together with a method for developing and evaluating service discovery systems. The first layer, which we call the Enhanced Traditional Layer (ETL), was studied by developing the ODEN system and including the ETL within it. ODEN extends the traditional, technical service discovery layer by introducing ontology-based semantics and reasoning engines. The second layer, the Relevant Service Discovery Layer, was explored by incorporating it into the MAGUBI system. MAGUBI addresses the human aspects in the challenge of relevant service discovery by employing common-sense models of user activities, domain knowledge, and world knowledge in combination with rule engines. The RESPONSORIA system provides a web-based evaluation platform with a desktop look and feel. This system explores service discovery in a service-oriented architecture setting. RESPONSORIA addresses a command and control scenario for rescue services where multiple actors and organizations work together at a municipal level. RESPONSORIA was the basis for the wizard of oz evaluation employing rescue services professionals. The result highlighted the importance of service naming and presentation to the user. Furthermore, there is disagreement among users regarding the optimal service recommendation, but the results indicated that good recommendations are valuable and the system can be seen as a partner.